@article{ref1,
title="Just in time crisis response: suicide alert system for telemedicine psychotherapy settings",
journal="Psychotherapy research",
year="2020",
author="Bantilan, Niels and Malgaroli, Matteo and Ray, Bonnie and Hull, Thomas D.",
volume="ePub",
number="ePub",
pages="1-11",
abstract="<b>Objective:</b> To design a Natural Language Processing (NLP) algorithm capable of detecting suicide content from patients' written communication to their therapists, to support rapid response and clinical decision making in telehealth settings. <b>Method:</b> A training dataset of therapy transcripts for 1,864 patients was established by detecting patient content endorsing suicidality using a proxy-model anchored on therapists' suicide prevention interventions; human expert raters then assessed the level of suicide risk endorsed by patients identified by the proxy-model (i.e., no risk, risk factors, ideation, method, or plan). A bag-of-words classification model was then iteratively built using the annotations from the expert raters to detect suicide risk level in 85,216 labeled patients' sentences from the training dataset. <b>Results:</b> The final NLP model identified risk-related content from non-risk content with good accuracy (AUC = 82.78). <b>Conclusions:</b> Risk for suicide could be reliably identified by the NLP algorithm. The risk detection model could assist telehealth clinicians in providing crisis resources in a timely manner. This modeling approach could also be applied to other psychotherapy research tasks to assist in the understanding of how the psychotherapy process unfolds for each patient and therapist.<p /> <p>Language: en</p>",
language="en",
issn="1050-3307",
doi="10.1080/10503307.2020.1781952",
url="http://dx.doi.org/10.1080/10503307.2020.1781952"
}